[BioC] Working with low abundance probesets !!

Sean Davis sdavis2 at mail.nih.gov
Thu Dec 1 15:04:30 CET 2005

On 12/1/05 8:50 AM, "Sharon Anbu" <sharonanandhi at gmail.com> wrote:

> Hi All,
> I am working with the Affy data set for which the max fold change is
> around 1.7.  Most of the probesets have a fold change between 1.2 and
> 1.5. I have done the standard data analysis procedure reccommanded by
> many people in Bioconductor (starting with RMA extraction, difference
> between 2-groups, t-test, p-val & corrected p-val etc). After doing
> this, I have got only 20 significant genes, which has no biological
> significance with the experiment.

How many arrays have you run?  If you know that you are looking for fold
changes of 1.2-1.7 (I'm not sure how you know which genes are true and
showing these changes, but...), you will likely need a stable system (little
biological variability within groups) or a "relatively" large n.  You could
do a simple power calculation to determine what your power is to detect a
difference between your two groups given a "typical" variance and with
different numbers of slides.  If you have good power with the number of
slides that you are using, then you may be seeing true negative results.  If
not, then increasing the number of arrays might be a good bet.  All that
said, if you have a set of genes that you are interested in, you could go
directly from "fold-change" on the array (ignore statistics such as p-value
altogether) and move directly to validation with PCR-based methods.


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